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Publication Open Access 3D microprinting of iron platinum nanoparticle-based magnetic mobile microrobots(Wiley, 2021) Giltinan, Joshua; Sridhar, Varun; Bozüyük, Uğur; Sheehan, Devin; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethylolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 mu m long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low-magnitude actuation fields of 5-10mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.Publication Open Access A computational multicriteria optimization approach to controller design for pysical human-robot interaction(Institute of Electrical and Electronics Engineers (IEEE), 2020) Tokatlı, Ozan; Patoğlu, Volkan; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.
Publication Metadata only A monolithic opto-coupler based sensor for contact force detection in artificial hand(Ieee, 2016) N/A; N/A; Department of Mechanical Engineering; Shams, Sarmad; Lazoğlu, İsmail; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391This paper presents a monolithic opto-coupler based force sensor design to detect the contact forces of the fingertip of the artificial hand during grasp process. Effective and precise measurement of the contact force is always a challenge for the humid and temperature varying environment. In this paper, we propose a novel design of force sensor with optical technique. The optical technique is preferred over other techniques because of its simpler electronics and less immunity to temperature variation under humid environment. Simulation results conducted using Finite Element Method (FEM) analysis confirmed the deflection is linear for the forces from 0 to +/- 100 N. The maximum stress found at 100 N is 252.39 MPa. Also, modal analysis is performed to ensure the sensor is durable and operative while handling different vibrating objects. Calibration experiment of the sensor is performed using multipoint calibration process and curve fitting technique.Publication Metadata only A monolithic opto-coupler based sensor for contact force detection in artificial hand(Institute of Electrical and Electronics Engineers (IEEE), 2016) N/A; Department of Mechanical Engineering; Shams, Sarmad; Lazoğlu, İsmail; PhD Student; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391This paper presents a monolithic opto-coupler based force sensor design to detect the contact forces of the fingertip of the artificial hand during grasp process. Effective and precise measurement of the contact force is always a challenge for the humid and temperature varying environment. In this paper, we propose a novel design of force sensor with optical technique. The optical technique is preferred over other techniques because of its simpler electronics and less immunity to temperature variation under humid environment. Simulation results conducted using Finite Element Method (FEM) analysis confirmed the deflection is linear for the forces from 0 to ±100 N. The maximum stress found at 100 N is 252.39 MPa. Also, modal analysis is performed to ensure the sensor is durable and operative while handling different vibrating objects. Calibration experiment of the sensor is performed using multipoint calibration process and curve fitting technique.Publication Metadata only A new control architecture for physical human-robot interaction based on haptic communication(Ieee, 2014) N/A; N/A; Department of Mechanical Engineering; Aydın, Yusuf; Arghavani, Nasser; Başdoğan, Çağatay; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328776; N/A; 125489In the near future, humans and robots are expected to perform collaborative tasks involving physical interaction in various different environments such as homes, hospitals, and factories. One important research topic in physical Human-Robot Interaction (pHRI) is to develop tacit and natural haptic communication between the partners. Although there are already several studies in the area of Human-Robot Interaction, the number of studies investigating the physical interaction between the partners and in particular the haptic communication are limited and the interaction in such systems is still artificial when compared to natural human-human collaboration. Although the tasks involving physical interaction such as the table transportation can be planned and executed naturally and intuitively by two humans, there are unfortunately no robots in the market that can collaborate and perform the same tasks with us. In this study, we propose a new controller for the robotic partner that is designed to a) detect the intentions of the human partner through haptic channel using a fuzzy controller b) adjust its contribution to the task via a variable impedance controller and c) resolve the conflicts during the task execution by controlling the internal forces. The results of the simulations performed in Simulink/Matlab show that the proposed controller is superior to the stand-alone standard/variable impedance controllers.Publication Metadata only A realistic simulation environment for mri-based robust control of untethered magnetic robots with intra-operational imaging(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Tiryaki, Mehmet Efe; Erin, Önder; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Dual-use of magnetic resonance imaging (MRI) devices for robot tracking and actuation has transformed them into potential medical robotics platforms for targeted therapies and minimally invasive surgeries. In this letter, we present the dynamic simulations of anMRI-based tracking and actuation scheme, which performs intra-operational imaging while controlling untethered magnetic robots. In our realistic rigid-body simulation, we show that the robot could be controlled with a 1D projection-based position feedback while performing intra-operational echo-planar imaging (EPI). From the simulations, we observe that the velocity estimation error is the main source of the controller instability for low MRI sequence frequencies. To minimize the velocity estimation errors, we constrain the controller gains according to maximum closed-loop rates achievable for different sequence durations. Using the constrained controller in simulations, we confirm that EPI imaging could be introduced to the sequence as an intra-operational imaging method. Although the intro-operational imaging increases the position estimation error to 2.0 mm for a simulated MRI-based position sensing with a 0.6 mm Gaussian noise, it does not cause controller instability up to 128 k-space lines.With the presented approach, continuous physiological images could be acquired during medical operations while a magnetic robot is actuated and tracked inside an MRI device.Publication Metadata only An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning(Elsevier, 2022) Aydin, Yusuf; N/A; N/A; N/A; Department of Mechanical Engineering; Güler, Berk; Niaz, Pouya Pourakbarian; Madani, Alireza; Başdoğan, Çağatay; Master Student; Master Student; Master Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 125489In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.Publication Metadata only BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching(American Association for the Advancement of Science (AAAS), 2022) Badri-Sprowitz, Alexander; Sarvestani, Alborz Aghamaleki; Daley, Monica A.; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to support the robot's mass during stance and the rapid cycling of the leg's state between stance and swing phases. Here, we demonstrate an avian-inspired robot leg design, BirdBot, that challenges the reliance on rapid feedback control for joint coordination and replaces active control with intrinsic, mechanical coupling, reminiscent of a self-engaging and disengaging clutch. A spring tendon network rapidly switches the leg's slack segments into a loadable state at touchdown, distributes load among joints, enables rapid disengagement at toe-off through elastically stored energy, and coordinates swing leg flexion. A bistable joint mediates the spring tendon network's disengagement at the end of stance, powered by stance phase leg angle progression. We show reduced knee-flexing torque to a 10th of what is required for a nonclutching, parallel-elastic leg design with the same kinematics, whereas spring-based compliance extends the leg in stance phase. These mechanisms enable bipedal locomotion with four robot actuators under feedforward control, with high energy efficiency. The robot offers a physical model demonstration of an avian-inspired, multiarticular elastic coupling mechanism that can achieve self-stable, robust, and economic legged locomotion with simple control and no sensory feedback. The proposed design is scalable, allowing the design of large legged robots. BirdBot demonstrates a mechanism for self-engaging and disengaging parallel elastic legs that are contact-triggered by the foot's own lever-arm action.Publication Open Access Control and transport of passive particles using self-organized spinning micro-disks(Institute of Electrical and Electronics Engineers (IEEE), 2022) Basualdo, Franco N. Pinan; Gardi, Gaurav; Wang, Wendong; Demir, Sinan O.; Bolopion, Aude; Gauthier, Michael; Lambert, Pierre; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; School of Medicine; 297104Traditional robotic systems have proven to be instrumental in object manipulation tasks for automated manufacturing processes. Object manipulation in such cases typically involves transport, pick-and-place and assembly of objects using automated conveyors and robotic arms. However, the forces at microscopic scales (e.g., surface tension, Van der Waals, electrostatic) can be qualitatively and quantitatively different from those at macroscopic scales. These forces render the release of objects difficult, and hence, traditional systems cannot be directly transferred to small scales (below a few millimeters). Consequently, novel micro-robotic manipulation systems have to be designed to take into account these scaling effects. Such systems could be beneficial for micro-fabrication processes and for biological studies. Here, we show autonomous position control of passive particles floating at the air-water interface using a collective of self-organized spinning micro-disks with a diameter of 300 mu m. First, we show that the spinning micro-disks collectives generate azimuthal flows that cause passive particles to orbit around them. We then develop a closed-loop controller to demonstrate autonomous position control of passive particles without physical contact. Finally, we showcase the capability of our system to split from an expanded to several circular collectives while holding the particle at a fixed target. Our system's contact-free object manipulation capability could be used for transporting delicate biological objects and for guiding self-assembly of passive objects for micro-fabrication.Publication Open Access Deep learning-based 3D magnetic microrobot tracking using 2D MR images(Institute of Electrical and Electronics Engineers (IEEE), 2022) Tiryaki, Mehmet Efe; Demir, Sinan Özgün; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; School of Medicine; 297104Magnetic resonance imaging (MRI)-guided robots emerged as a promising tool for minimally invasive medical operations. Recently, MRI scanners have been proposed for actuating and localizing magnetic microrobots in the patient's body using two-dimensional (2D) MR images. However, three-dimensional (3D) magnetic microrobots tracking during motion is still an untackled issue in MRI-powered microrobotics. Here, we present a deep learning-based 3D magnetic microrobot tracking method using 2D MR images during microrobot motion. The proposed method comprises a convolutional neural network (CNN) and complementary particle filter for 3D microrobot tracking. The CNN localizes the microrobot position relative to the 2D MRI slice and classifies the microrobot visibility in the MR images. First, we create an ultrasound (US) imaging-mentored MRI-based microrobot imaging and actuation system to train the CNN. Then, we trained the CNN using the MRI data generated by automated experiments using US image-based visual servoing of a microrobot with a 500 mu m-diameter magnetic core. We showed that the proposed CNN can localize the microrobot and classified its visibility in an in vitro environment with +/- 0.56 mm and 87.5% accuracy in 2D MR images, respectively. Furthermore, we demonstrated ex-vivo 3D microrobot tracking with +/- 1.43 mm accuracy, improving tracking accuracy by 60% compared to the previous studies. The presented tracking strategy will enable MRI-powered microrobots to be used in high-precision targeted medical applications in the future.